def validate_autoscale_timegrain(namespace): from azure.mgmt.monitor.models import MetricTrigger from azure.cli.command_modules.monitor.actions import period_type from azure.cli.command_modules.monitor.util import get_autoscale_statistic_map values = namespace.timegrain if len(values) == 1: # workaround because CMD.exe eats > character... Allows condition to be # specified as a quoted expression values = values[0].split(' ') name_offset = 0 try: time_grain = period_type(values[1]) name_offset += 1 except ValueError: time_grain = period_type('1m') try: statistic = get_autoscale_statistic_map()[values[0]] name_offset += 1 except KeyError: statistic = get_autoscale_statistic_map()['avg'] timegrain = MetricTrigger(metric_name=None, metric_resource_uri=None, time_grain=time_grain, statistic=statistic, time_window=None, time_aggregation=None, operator=None, threshold=None) namespace.timegrain = timegrain
def validate_autoscale_timegrain(namespace): from azure.mgmt.monitor.models import MetricTrigger from azure.cli.command_modules.monitor.actions import period_type from azure.cli.command_modules.monitor.util import get_autoscale_statistic_map values = namespace.timegrain if len(values) == 1: # workaround because CMD.exe eats > character... Allows condition to be # specified as a quoted expression values = values[0].split(' ') name_offset = 0 try: time_grain = period_type(values[1]) name_offset += 1 except ValueError: time_grain = period_type('1m') try: statistic = get_autoscale_statistic_map()[values[0]] name_offset += 1 except KeyError: statistic = get_autoscale_statistic_map()['avg'] timegrain = MetricTrigger( metric_name=None, metric_resource_uri=None, time_grain=time_grain, statistic=statistic, time_window=None, time_aggregation=None, operator=None, threshold=None ) namespace.timegrain = timegrain